{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from keras import backend as K\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 从训练集取样本\n",
    "# samples_data_root = 'D:/Data/mc_data/train/images/0/'\n",
    "\n",
    "# filenames = np.random.choice(os.listdir(samples_data_root), 1000)\n",
    "# samples = []\n",
    "# for fn in filenames:\n",
    "#     fullname = samples_data_root + fn\n",
    "#     image = Image.open(fullname)\n",
    "#     image_arr = np.array(image)\n",
    "#     samples.append(image_arr)\n",
    "\n",
    "# sample_images = np.array(samples)\n",
    "# print(sample_images.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_data(img,mask):\n",
    "    mean_ = np.array([77.79408427,  89.68194542, 101.41694189])\n",
    "    img = (img - mean_) / 255\n",
    "    mask = mask /255\n",
    "    mask[mask > 0.5] = 1\n",
    "    mask[mask <= 0.5] = 0\n",
    "    return (img,mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_generator():\n",
    "    train_data_root = 'D:/Data/mc_data/train'  # 训练集存放路径\n",
    "    val_data_root = 'D:/Data/mc_data/val'  # 验证集存放路径\n",
    "\n",
    "    # data_gen_args = dict(rotation_range=0.2,\n",
    "    #                     width_shift_range=0.05,\n",
    "    #                     height_shift_range=0.05,\n",
    "    #                     shear_range=0.05,\n",
    "    #                     zoom_range=0.05,\n",
    "    #                     horizontal_flip=True,\n",
    "    #                     fill_mode='nearest')\n",
    "    data_gen_args = dict()\n",
    "\n",
    "    train_image_datagen = ImageDataGenerator(**data_gen_args)\n",
    "    train_mask_datagen = ImageDataGenerator(**data_gen_args)\n",
    "\n",
    "    seed=1\n",
    "\n",
    "    train_image_generator = train_image_datagen.flow_from_directory(\n",
    "        train_data_root + '/images',\n",
    "        target_size=(288,288),\n",
    "        class_mode=None,\n",
    "        batch_size=16,\n",
    "        seed=seed,\n",
    "        color_mode='rgb')\n",
    "\n",
    "    train_mask_generator = train_mask_datagen.flow_from_directory(\n",
    "        train_data_root + '/masks',\n",
    "        target_size=(288,288),\n",
    "        class_mode=None,\n",
    "        batch_size=16,\n",
    "        seed=seed,\n",
    "        color_mode='grayscale')\n",
    "\n",
    "    # combine generators into one which yields image and masks\n",
    "    train_generator = zip(train_image_generator, train_mask_generator)\n",
    "    \n",
    "    for (image, mask) in train_generator:\n",
    "        print(image.shape)\n",
    "        print(mask.shape)\n",
    "        yield adjust_data(image, mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 280741 images belonging to 1 classes.\n",
      "Found 280741 images belonging to 1 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 288, 288, 3)\n",
      "(16, 288, 288, 1)\n",
      "(288, 288, 3)\n",
      "(288, 288, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "for images,masks in make_train_generator():\n",
    "    plt.subplot(121)\n",
    "    print(images[0].shape)\n",
    "    plt.imshow(images[0])\n",
    "    plt.subplot(122)\n",
    "    print(masks[0].shape)\n",
    "    mask = K.eval(K.squeeze(masks[0],axis=2))\n",
    "    plt.imshow(mask, cmap='gray')\n",
    "    break\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 288, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = K.squeeze(masks[0],axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Squeeze_1:0' shape=(288, 288) dtype=float32>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "with tf.Session() as sess:\n",
    "    test = mask.eval()\n",
    "    np.savetxt('test.txt',test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
